Overview

Dataset statistics

Number of variables37
Number of observations4424
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory296.0 B

Variable types

Numeric28
Categorical9

Alerts

Application mode is highly correlated with Age at enrollmentHigh correlation
Previous qualification (grade) is highly correlated with Admission gradeHigh correlation
Nacionality is highly correlated with InternationalHigh correlation
Admission grade is highly correlated with Previous qualification (grade)High correlation
Age at enrollment is highly correlated with Application modeHigh correlation
International is highly correlated with NacionalityHigh correlation
Curricular units 1st sem (credited) is highly correlated with Curricular units 2nd sem (credited)High correlation
Curricular units 1st sem (enrolled) is highly correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Curricular units 1st sem (evaluations) is highly correlated with Curricular units 2nd sem (evaluations)High correlation
Curricular units 1st sem (approved) is highly correlated with Curricular units 1st sem (enrolled) and 4 other fieldsHigh correlation
Curricular units 1st sem (grade) is highly correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Curricular units 2nd sem (credited) is highly correlated with Curricular units 1st sem (credited)High correlation
Curricular units 2nd sem (enrolled) is highly correlated with Curricular units 1st sem (enrolled) and 2 other fieldsHigh correlation
Curricular units 2nd sem (evaluations) is highly correlated with Curricular units 1st sem (evaluations)High correlation
Curricular units 2nd sem (approved) is highly correlated with Curricular units 1st sem (enrolled) and 4 other fieldsHigh correlation
Curricular units 2nd sem (grade) is highly correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Marital status is highly correlated with Age at enrollmentHigh correlation
Application mode is highly correlated with Age at enrollmentHigh correlation
Previous qualification (grade) is highly correlated with Admission gradeHigh correlation
Nacionality is highly correlated with InternationalHigh correlation
Mother's qualification is highly correlated with Father's qualificationHigh correlation
Father's qualification is highly correlated with Mother's qualificationHigh correlation
Mother's occupation is highly correlated with Father's occupationHigh correlation
Father's occupation is highly correlated with Mother's occupationHigh correlation
Admission grade is highly correlated with Previous qualification (grade)High correlation
Age at enrollment is highly correlated with Marital status and 1 other fieldsHigh correlation
International is highly correlated with NacionalityHigh correlation
Curricular units 1st sem (credited) is highly correlated with Curricular units 1st sem (enrolled) and 4 other fieldsHigh correlation
Curricular units 1st sem (enrolled) is highly correlated with Curricular units 1st sem (credited) and 6 other fieldsHigh correlation
Curricular units 1st sem (evaluations) is highly correlated with Curricular units 1st sem (credited) and 5 other fieldsHigh correlation
Curricular units 1st sem (approved) is highly correlated with Curricular units 1st sem (credited) and 8 other fieldsHigh correlation
Curricular units 1st sem (grade) is highly correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Curricular units 1st sem (without evaluations) is highly correlated with Curricular units 2nd sem (without evaluations)High correlation
Curricular units 2nd sem (credited) is highly correlated with Curricular units 1st sem (credited) and 5 other fieldsHigh correlation
Curricular units 2nd sem (enrolled) is highly correlated with Curricular units 1st sem (credited) and 6 other fieldsHigh correlation
Curricular units 2nd sem (evaluations) is highly correlated with Curricular units 1st sem (enrolled) and 3 other fieldsHigh correlation
Curricular units 2nd sem (approved) is highly correlated with Curricular units 1st sem (enrolled) and 5 other fieldsHigh correlation
Curricular units 2nd sem (grade) is highly correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Curricular units 2nd sem (without evaluations) is highly correlated with Curricular units 1st sem (without evaluations)High correlation
Nacionality is highly correlated with InternationalHigh correlation
International is highly correlated with NacionalityHigh correlation
Curricular units 1st sem (credited) is highly correlated with Curricular units 2nd sem (credited)High correlation
Curricular units 1st sem (enrolled) is highly correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Curricular units 1st sem (evaluations) is highly correlated with Curricular units 2nd sem (evaluations)High correlation
Curricular units 1st sem (approved) is highly correlated with Curricular units 1st sem (enrolled) and 4 other fieldsHigh correlation
Curricular units 1st sem (grade) is highly correlated with Curricular units 1st sem (approved) and 1 other fieldsHigh correlation
Curricular units 2nd sem (credited) is highly correlated with Curricular units 1st sem (credited)High correlation
Curricular units 2nd sem (enrolled) is highly correlated with Curricular units 1st sem (enrolled) and 2 other fieldsHigh correlation
Curricular units 2nd sem (evaluations) is highly correlated with Curricular units 1st sem (evaluations)High correlation
Curricular units 2nd sem (approved) is highly correlated with Curricular units 1st sem (enrolled) and 3 other fieldsHigh correlation
Curricular units 2nd sem (grade) is highly correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Marital status is highly correlated with Daytime/evening attendance and 1 other fieldsHigh correlation
Application mode is highly correlated with Previous qualification and 1 other fieldsHigh correlation
Application order is highly correlated with DisplacedHigh correlation
Course is highly correlated with Curricular units 1st sem (enrolled) and 5 other fieldsHigh correlation
Daytime/evening attendance is highly correlated with Marital status and 1 other fieldsHigh correlation
Previous qualification is highly correlated with Application modeHigh correlation
Previous qualification (grade) is highly correlated with Admission gradeHigh correlation
Nacionality is highly correlated with InternationalHigh correlation
Mother's qualification is highly correlated with Father's qualificationHigh correlation
Father's qualification is highly correlated with Mother's qualificationHigh correlation
Mother's occupation is highly correlated with Father's occupationHigh correlation
Father's occupation is highly correlated with Mother's occupationHigh correlation
Admission grade is highly correlated with Previous qualification (grade)High correlation
Displaced is highly correlated with Application order and 1 other fieldsHigh correlation
Debtor is highly correlated with Tuition fees up to dateHigh correlation
Tuition fees up to date is highly correlated with DebtorHigh correlation
Age at enrollment is highly correlated with Marital status and 3 other fieldsHigh correlation
International is highly correlated with NacionalityHigh correlation
Curricular units 1st sem (credited) is highly correlated with Curricular units 1st sem (enrolled) and 6 other fieldsHigh correlation
Curricular units 1st sem (enrolled) is highly correlated with Course and 7 other fieldsHigh correlation
Curricular units 1st sem (evaluations) is highly correlated with Course and 11 other fieldsHigh correlation
Curricular units 1st sem (approved) is highly correlated with Curricular units 1st sem (credited) and 9 other fieldsHigh correlation
Curricular units 1st sem (grade) is highly correlated with Course and 6 other fieldsHigh correlation
Curricular units 1st sem (without evaluations) is highly correlated with Curricular units 1st sem (evaluations) and 2 other fieldsHigh correlation
Curricular units 2nd sem (credited) is highly correlated with Curricular units 1st sem (credited) and 6 other fieldsHigh correlation
Curricular units 2nd sem (enrolled) is highly correlated with Course and 7 other fieldsHigh correlation
Curricular units 2nd sem (evaluations) is highly correlated with Course and 11 other fieldsHigh correlation
Curricular units 2nd sem (approved) is highly correlated with Curricular units 1st sem (credited) and 9 other fieldsHigh correlation
Curricular units 2nd sem (grade) is highly correlated with Course and 6 other fieldsHigh correlation
Curricular units 2nd sem (without evaluations) is highly correlated with Curricular units 1st sem (without evaluations) and 1 other fieldsHigh correlation
Unemployment rate is highly correlated with Inflation rate and 1 other fieldsHigh correlation
Inflation rate is highly correlated with Unemployment rate and 1 other fieldsHigh correlation
GDP is highly correlated with Unemployment rate and 1 other fieldsHigh correlation
Target is highly correlated with Curricular units 1st sem (evaluations) and 4 other fieldsHigh correlation
Mother's occupation has 144 (3.3%) zeros Zeros
Father's occupation has 128 (2.9%) zeros Zeros
Curricular units 1st sem (credited) has 3847 (87.0%) zeros Zeros
Curricular units 1st sem (enrolled) has 180 (4.1%) zeros Zeros
Curricular units 1st sem (evaluations) has 349 (7.9%) zeros Zeros
Curricular units 1st sem (approved) has 718 (16.2%) zeros Zeros
Curricular units 1st sem (grade) has 718 (16.2%) zeros Zeros
Curricular units 1st sem (without evaluations) has 4130 (93.4%) zeros Zeros
Curricular units 2nd sem (credited) has 3894 (88.0%) zeros Zeros
Curricular units 2nd sem (enrolled) has 180 (4.1%) zeros Zeros
Curricular units 2nd sem (evaluations) has 401 (9.1%) zeros Zeros
Curricular units 2nd sem (approved) has 870 (19.7%) zeros Zeros
Curricular units 2nd sem (grade) has 870 (19.7%) zeros Zeros
Curricular units 2nd sem (without evaluations) has 4142 (93.6%) zeros Zeros

Reproduction

Analysis started2022-08-23 13:30:46.517625
Analysis finished2022-08-23 13:32:58.703473
Duration2 minutes and 12.19 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Marital status
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.178571429
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:00.830081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6057469461
Coefficient of variation (CV)0.5139671058
Kurtosis21.4826393
Mean1.178571429
Median Absolute Deviation (MAD)0
Skewness4.39976435
Sum5214
Variance0.3669293627
MonotonicityNot monotonic
2022-08-23T19:33:01.478330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
13919
88.6%
2379
 
8.6%
491
 
2.1%
525
 
0.6%
66
 
0.1%
34
 
0.1%
ValueCountFrequency (%)
13919
88.6%
2379
 
8.6%
34
 
0.1%
491
 
2.1%
525
 
0.6%
66
 
0.1%
ValueCountFrequency (%)
66
 
0.1%
525
 
0.6%
491
 
2.1%
34
 
0.1%
2379
 
8.6%
13919
88.6%

Application mode
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.66907776
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:01.582336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median17
Q339
95-th percentile44
Maximum57
Range56
Interquartile range (IQR)38

Descriptive statistics

Standard deviation17.48468229
Coefficient of variation (CV)0.9365584373
Kurtosis-1.453806079
Mean18.66907776
Median Absolute Deviation (MAD)16
Skewness0.39303572
Sum82592
Variance305.7141148
MonotonicityNot monotonic
2022-08-23T19:33:01.675946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
11708
38.6%
17872
19.7%
39785
17.7%
43312
 
7.1%
44213
 
4.8%
7139
 
3.1%
18124
 
2.8%
4277
 
1.7%
5159
 
1.3%
1638
 
0.9%
Other values (8)97
 
2.2%
ValueCountFrequency (%)
11708
38.6%
23
 
0.1%
516
 
0.4%
7139
 
3.1%
1010
 
0.2%
1530
 
0.7%
1638
 
0.9%
17872
19.7%
18124
 
2.8%
261
 
< 0.1%
ValueCountFrequency (%)
571
 
< 0.1%
5335
 
0.8%
5159
 
1.3%
44213
 
4.8%
43312
 
7.1%
4277
 
1.7%
39785
17.7%
271
 
< 0.1%
261
 
< 0.1%
18124
 
2.8%

Application order
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.727848101
Minimum0
Maximum9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:01.771681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.313793078
Coefficient of variation (CV)0.7603637596
Kurtosis2.651288656
Mean1.727848101
Median Absolute Deviation (MAD)0
Skewness1.881049957
Sum7644
Variance1.726052253
MonotonicityNot monotonic
2022-08-23T19:33:01.860633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
13026
68.4%
2547
 
12.4%
3309
 
7.0%
4249
 
5.6%
5154
 
3.5%
6137
 
3.1%
91
 
< 0.1%
01
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
13026
68.4%
2547
 
12.4%
3309
 
7.0%
4249
 
5.6%
5154
 
3.5%
6137
 
3.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
6137
 
3.1%
5154
 
3.5%
4249
 
5.6%
3309
 
7.0%
2547
 
12.4%
13026
68.4%
01
 
< 0.1%

Course
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8856.642631
Minimum33
Maximum9991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:01.955986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile171
Q19085
median9238
Q39556
95-th percentile9991
Maximum9991
Range9958
Interquartile range (IQR)471

Descriptive statistics

Standard deviation2063.566416
Coefficient of variation (CV)0.232996464
Kurtosis13.19914907
Mean8856.642631
Median Absolute Deviation (MAD)262
Skewness-3.809135181
Sum39181787
Variance4258306.354
MonotonicityNot monotonic
2022-08-23T19:33:02.054986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
9500766
17.3%
9147380
 
8.6%
9238355
 
8.0%
9085337
 
7.6%
9773331
 
7.5%
9670268
 
6.1%
9991268
 
6.1%
9254252
 
5.7%
9070226
 
5.1%
171215
 
4.9%
Other values (7)1026
23.2%
ValueCountFrequency (%)
3312
 
0.3%
171215
4.9%
8014215
4.9%
9003210
4.7%
9070226
5.1%
9085337
7.6%
9119170
3.8%
9130141
 
3.2%
9147380
8.6%
9238355
8.0%
ValueCountFrequency (%)
9991268
 
6.1%
9853192
 
4.3%
9773331
7.5%
9670268
 
6.1%
955686
 
1.9%
9500766
17.3%
9254252
 
5.7%
9238355
8.0%
9147380
8.6%
9130141
 
3.2%

Daytime/evening attendance
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
1
3941 
0
483 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
13941
89.1%
0483
 
10.9%

Length

2022-08-23T19:33:02.156992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-23T19:33:02.703003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
13941
89.1%
0483
 
10.9%

Most occurring characters

ValueCountFrequency (%)
13941
89.1%
0483
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13941
89.1%
0483
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
13941
89.1%
0483
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13941
89.1%
0483
 
10.9%

Previous qualification
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.577757685
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:02.849128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile39
Maximum43
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.21659234
Coefficient of variation (CV)2.231789676
Kurtosis6.778166218
Mean4.577757685
Median Absolute Deviation (MAD)0
Skewness2.871206778
Sum20252
Variance104.3787591
MonotonicityNot monotonic
2022-08-23T19:33:02.990130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13717
84.0%
39219
 
5.0%
19162
 
3.7%
3126
 
2.8%
1245
 
1.0%
4040
 
0.9%
4236
 
0.8%
223
 
0.5%
616
 
0.4%
911
 
0.2%
Other values (7)29
 
0.7%
ValueCountFrequency (%)
13717
84.0%
223
 
0.5%
3126
 
2.8%
48
 
0.2%
51
 
< 0.1%
616
 
0.4%
911
 
0.2%
104
 
0.1%
1245
 
1.0%
141
 
< 0.1%
ValueCountFrequency (%)
436
 
0.1%
4236
 
0.8%
4040
 
0.9%
39219
5.0%
387
 
0.2%
19162
3.7%
152
 
< 0.1%
141
 
< 0.1%
1245
 
1.0%
104
 
0.1%

Previous qualification (grade)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct101
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.6133137
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:03.258905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile110
Q1125
median133.1
Q3140
95-th percentile157
Maximum190
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.18833169
Coefficient of variation (CV)0.09944952972
Kurtosis0.9682577181
Mean132.6133137
Median Absolute Deviation (MAD)7.1
Skewness0.3128674881
Sum586681.3
Variance173.9320927
MonotonicityNot monotonic
2022-08-23T19:33:03.422017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133.1491
 
11.1%
130375
 
8.5%
140336
 
7.6%
120278
 
6.3%
150162
 
3.7%
125122
 
2.8%
135108
 
2.4%
110101
 
2.3%
13199
 
2.2%
16095
 
2.1%
Other values (91)2257
51.0%
ValueCountFrequency (%)
951
 
< 0.1%
962
 
< 0.1%
971
 
< 0.1%
992
 
< 0.1%
10076
1.7%
1016
 
0.1%
1025
 
0.1%
1033
 
0.1%
1054
 
0.1%
10610
 
0.2%
ValueCountFrequency (%)
1902
 
< 0.1%
1881
 
< 0.1%
184.41
 
< 0.1%
1821
 
< 0.1%
1809
0.2%
1782
 
< 0.1%
1772
 
< 0.1%
1761
 
< 0.1%
1751
 
< 0.1%
1741
 
< 0.1%

Nacionality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.873191682
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:03.541543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum109
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.914514032
Coefficient of variation (CV)3.691300842
Kurtosis135.1462064
Mean1.873191682
Median Absolute Deviation (MAD)0
Skewness10.70399768
Sum8287
Variance47.8105043
MonotonicityNot monotonic
2022-08-23T19:33:03.639867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
14314
97.5%
4138
 
0.9%
2614
 
0.3%
2213
 
0.3%
613
 
0.3%
245
 
0.1%
1003
 
0.1%
113
 
0.1%
1033
 
0.1%
212
 
< 0.1%
Other values (11)16
 
0.4%
ValueCountFrequency (%)
14314
97.5%
22
 
< 0.1%
613
 
0.3%
113
 
0.1%
131
 
< 0.1%
141
 
< 0.1%
171
 
< 0.1%
212
 
< 0.1%
2213
 
0.3%
245
 
0.1%
ValueCountFrequency (%)
1091
 
< 0.1%
1081
 
< 0.1%
1052
 
< 0.1%
1033
 
0.1%
1012
 
< 0.1%
1003
 
0.1%
622
 
< 0.1%
4138
0.9%
321
 
< 0.1%
2614
 
0.3%

Mother's qualification
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.5619349
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:03.746021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median19
Q337
95-th percentile38
Maximum44
Range43
Interquartile range (IQR)35

Descriptive statistics

Standard deviation15.60318632
Coefficient of variation (CV)0.7976300096
Kurtosis-1.692292418
Mean19.5619349
Median Absolute Deviation (MAD)18
Skewness0.001978477864
Sum86542
Variance243.4594234
MonotonicityNot monotonic
2022-08-23T19:33:03.873675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
11069
24.2%
371009
22.8%
19953
21.5%
38562
12.7%
3438
9.9%
34130
 
2.9%
283
 
1.9%
449
 
1.1%
1242
 
0.9%
521
 
0.5%
Other values (19)68
 
1.5%
ValueCountFrequency (%)
11069
24.2%
283
 
1.9%
3438
9.9%
449
 
1.1%
521
 
0.5%
64
 
0.1%
98
 
0.2%
103
 
0.1%
113
 
0.1%
1242
 
0.9%
ValueCountFrequency (%)
441
 
< 0.1%
434
 
0.1%
424
 
0.1%
416
 
0.1%
409
 
0.2%
398
 
0.2%
38562
12.7%
371009
22.8%
363
 
0.1%
353
 
0.1%

Father's qualification
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.27531646
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:03.990634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median19
Q337
95-th percentile38
Maximum44
Range43
Interquartile range (IQR)34

Descriptive statistics

Standard deviation15.34310781
Coefficient of variation (CV)0.6887941567
Kurtosis-1.580591792
Mean22.27531646
Median Absolute Deviation (MAD)18
Skewness-0.2986972192
Sum98546
Variance235.4109574
MonotonicityNot monotonic
2022-08-23T19:33:04.286678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
371209
27.3%
19968
21.9%
1904
20.4%
38702
15.9%
3282
 
6.4%
34112
 
2.5%
268
 
1.5%
439
 
0.9%
1238
 
0.9%
3920
 
0.5%
Other values (24)82
 
1.9%
ValueCountFrequency (%)
1904
20.4%
268
 
1.5%
3282
 
6.4%
439
 
0.9%
518
 
0.4%
62
 
< 0.1%
95
 
0.1%
102
 
< 0.1%
1110
 
0.2%
1238
 
0.9%
ValueCountFrequency (%)
441
 
< 0.1%
432
 
< 0.1%
421
 
< 0.1%
412
 
< 0.1%
405
 
0.1%
3920
 
0.5%
38702
15.9%
371209
27.3%
368
 
0.2%
352
 
< 0.1%

Mother's occupation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.96089512
Minimum0
Maximum194
Zeros144
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:04.404333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median5
Q39
95-th percentile9
Maximum194
Range194
Interquartile range (IQR)5

Descriptive statistics

Standard deviation26.41825291
Coefficient of variation (CV)2.410227689
Kurtosis29.22614507
Mean10.96089512
Median Absolute Deviation (MAD)3
Skewness5.339227055
Sum48491
Variance697.9240866
MonotonicityNot monotonic
2022-08-23T19:33:04.512377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
91577
35.6%
4817
18.5%
5530
 
12.0%
3351
 
7.9%
2318
 
7.2%
7272
 
6.1%
0144
 
3.3%
1102
 
2.3%
691
 
2.1%
9070
 
1.6%
Other values (22)152
 
3.4%
ValueCountFrequency (%)
0144
 
3.3%
1102
 
2.3%
2318
 
7.2%
3351
 
7.9%
4817
18.5%
5530
 
12.0%
691
 
2.1%
7272
 
6.1%
836
 
0.8%
91577
35.6%
ValueCountFrequency (%)
19411
0.2%
1934
 
0.1%
1925
 
0.1%
19126
0.6%
1755
 
0.1%
1731
 
< 0.1%
1711
 
< 0.1%
1532
 
< 0.1%
1522
 
< 0.1%
1513
 
0.1%

Father's occupation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.03232369
Minimum0
Maximum195
Zeros128
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:04.642442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q39
95-th percentile10
Maximum195
Range195
Interquartile range (IQR)5

Descriptive statistics

Standard deviation25.26304024
Coefficient of variation (CV)2.289911079
Kurtosis29.92739539
Mean11.03232369
Median Absolute Deviation (MAD)2
Skewness5.395173171
Sum48807
Variance638.2212023
MonotonicityNot monotonic
2022-08-23T19:33:04.769391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
91010
22.8%
7666
15.1%
5516
11.7%
4386
 
8.7%
3384
 
8.7%
8318
 
7.2%
10266
 
6.0%
6242
 
5.5%
2197
 
4.5%
1134
 
3.0%
Other values (36)305
 
6.9%
ValueCountFrequency (%)
0128
 
2.9%
1134
 
3.0%
2197
 
4.5%
3384
 
8.7%
4386
 
8.7%
5516
11.7%
6242
 
5.5%
7666
15.1%
8318
 
7.2%
91010
22.8%
ValueCountFrequency (%)
1951
 
< 0.1%
1942
 
< 0.1%
19315
0.3%
1926
 
0.1%
1833
 
0.1%
1822
 
< 0.1%
1813
 
0.1%
1754
 
0.1%
1741
 
< 0.1%
1722
 
< 0.1%

Admission grade
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct620
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.9781193
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:04.905428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile103.415
Q1117.9
median126.1
Q3134.8
95-th percentile153.5
Maximum190
Range95
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation14.48200082
Coefficient of variation (CV)0.1140511522
Kurtosis0.6627245994
Mean126.9781193
Median Absolute Deviation (MAD)8.4
Skewness0.5305998611
Sum561751.2
Variance209.7283477
MonotonicityNot monotonic
2022-08-23T19:33:05.034391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130162
 
3.7%
140153
 
3.5%
120145
 
3.3%
100116
 
2.6%
15081
 
1.8%
11077
 
1.7%
16043
 
1.0%
128.239
 
0.9%
12326
 
0.6%
12826
 
0.6%
Other values (610)3556
80.4%
ValueCountFrequency (%)
9511
0.2%
95.11
 
< 0.1%
95.52
 
< 0.1%
95.81
 
< 0.1%
967
0.2%
96.11
 
< 0.1%
96.71
 
< 0.1%
976
0.1%
97.21
 
< 0.1%
97.41
 
< 0.1%
ValueCountFrequency (%)
1903
0.1%
184.41
 
< 0.1%
1841
 
< 0.1%
183.51
 
< 0.1%
180.41
 
< 0.1%
1804
0.1%
179.61
 
< 0.1%
178.31
 
< 0.1%
1781
 
< 0.1%
176.71
 
< 0.1%

Displaced
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
1
2426 
0
1998 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
12426
54.8%
01998
45.2%

Length

2022-08-23T19:33:05.146426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-23T19:33:05.243428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
12426
54.8%
01998
45.2%

Most occurring characters

ValueCountFrequency (%)
12426
54.8%
01998
45.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12426
54.8%
01998
45.2%

Most occurring scripts

ValueCountFrequency (%)
Common4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12426
54.8%
01998
45.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12426
54.8%
01998
45.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
4373 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04373
98.8%
151
 
1.2%

Length

2022-08-23T19:33:05.328441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-23T19:33:05.423425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
04373
98.8%
151
 
1.2%

Most occurring characters

ValueCountFrequency (%)
04373
98.8%
151
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04373
98.8%
151
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04373
98.8%
151
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04373
98.8%
151
 
1.2%

Debtor
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
3921 
1
503 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03921
88.6%
1503
 
11.4%

Length

2022-08-23T19:33:05.504429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-23T19:33:05.601425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
03921
88.6%
1503
 
11.4%

Most occurring characters

ValueCountFrequency (%)
03921
88.6%
1503
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03921
88.6%
1503
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03921
88.6%
1503
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03921
88.6%
1503
 
11.4%

Tuition fees up to date
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
1
3896 
0
528 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13896
88.1%
0528
 
11.9%

Length

2022-08-23T19:33:05.685386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-23T19:33:05.814379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
13896
88.1%
0528
 
11.9%

Most occurring characters

ValueCountFrequency (%)
13896
88.1%
0528
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13896
88.1%
0528
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
13896
88.1%
0528
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13896
88.1%
0528
 
11.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
2868 
1
1556 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02868
64.8%
11556
35.2%

Length

2022-08-23T19:33:05.898381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-23T19:33:05.997349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
02868
64.8%
11556
35.2%

Most occurring characters

ValueCountFrequency (%)
02868
64.8%
11556
35.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02868
64.8%
11556
35.2%

Most occurring scripts

ValueCountFrequency (%)
Common4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02868
64.8%
11556
35.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02868
64.8%
11556
35.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
3325 
1
1099 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03325
75.2%
11099
 
24.8%

Length

2022-08-23T19:33:06.081381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-23T19:33:06.233850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
03325
75.2%
11099
 
24.8%

Most occurring characters

ValueCountFrequency (%)
03325
75.2%
11099
 
24.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03325
75.2%
11099
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
Common4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03325
75.2%
11099
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03325
75.2%
11099
 
24.8%

Age at enrollment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.26514467
Minimum17
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:06.336841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q119
median20
Q325
95-th percentile41
Maximum70
Range53
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.587815615
Coefficient of variation (CV)0.3261452153
Kurtosis4.12689183
Mean23.26514467
Median Absolute Deviation (MAD)2
Skewness2.054988369
Sum102925
Variance57.57494581
MonotonicityNot monotonic
2022-08-23T19:33:06.465167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
181036
23.4%
19911
20.6%
20599
13.5%
21322
 
7.3%
22174
 
3.9%
24131
 
3.0%
23108
 
2.4%
2694
 
2.1%
2593
 
2.1%
2791
 
2.1%
Other values (36)865
19.6%
ValueCountFrequency (%)
175
 
0.1%
181036
23.4%
19911
20.6%
20599
13.5%
21322
 
7.3%
22174
 
3.9%
23108
 
2.4%
24131
 
3.0%
2593
 
2.1%
2694
 
2.1%
ValueCountFrequency (%)
701
 
< 0.1%
621
 
< 0.1%
611
 
< 0.1%
602
 
< 0.1%
593
0.1%
583
0.1%
572
 
< 0.1%
555
0.1%
547
0.2%
537
0.2%

International
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
4314 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04314
97.5%
1110
 
2.5%

Length

2022-08-23T19:33:06.639608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-23T19:33:06.735548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
04314
97.5%
1110
 
2.5%

Most occurring characters

ValueCountFrequency (%)
04314
97.5%
1110
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04314
97.5%
1110
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common4424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04314
97.5%
1110
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04314
97.5%
1110
 
2.5%

Curricular units 1st sem (credited)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7099909584
Minimum0
Maximum20
Zeros3847
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:06.822514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.360506619
Coefficient of variation (CV)3.324699549
Kurtosis19.2057265
Mean0.7099909584
Median Absolute Deviation (MAD)0
Skewness4.169048768
Sum3141
Variance5.571991499
MonotonicityNot monotonic
2022-08-23T19:33:06.930508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
03847
87.0%
294
 
2.1%
185
 
1.9%
369
 
1.6%
651
 
1.2%
447
 
1.1%
741
 
0.9%
541
 
0.9%
831
 
0.7%
927
 
0.6%
Other values (11)91
 
2.1%
ValueCountFrequency (%)
03847
87.0%
185
 
1.9%
294
 
2.1%
369
 
1.6%
447
 
1.1%
541
 
0.9%
651
 
1.2%
741
 
0.9%
831
 
0.7%
927
 
0.6%
ValueCountFrequency (%)
202
 
< 0.1%
192
 
< 0.1%
184
 
0.1%
173
 
0.1%
163
 
0.1%
155
 
0.1%
1415
0.3%
1313
0.3%
1212
0.3%
1117
0.4%

Curricular units 1st sem (enrolled)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.27056962
Minimum0
Maximum26
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:07.042513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q15
median6
Q37
95-th percentile11
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.480178175
Coefficient of variation (CV)0.3955267744
Kurtosis8.937915353
Mean6.27056962
Median Absolute Deviation (MAD)1
Skewness1.619040906
Sum27741
Variance6.151283781
MonotonicityNot monotonic
2022-08-23T19:33:07.148854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
61910
43.2%
51010
22.8%
7656
 
14.8%
8296
 
6.7%
0180
 
4.1%
1266
 
1.5%
1052
 
1.2%
1145
 
1.0%
936
 
0.8%
1525
 
0.6%
Other values (13)148
 
3.3%
ValueCountFrequency (%)
0180
 
4.1%
17
 
0.2%
29
 
0.2%
310
 
0.2%
421
 
0.5%
51010
22.8%
61910
43.2%
7656
 
14.8%
8296
 
6.7%
936
 
0.8%
ValueCountFrequency (%)
261
 
< 0.1%
232
 
< 0.1%
216
 
0.1%
192
 
< 0.1%
1819
0.4%
1716
0.4%
1613
0.3%
1525
0.6%
1422
0.5%
1320
0.5%

Curricular units 1st sem (evaluations)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct35
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.299050633
Minimum0
Maximum45
Zeros349
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:07.261819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum45
Range45
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.179105569
Coefficient of variation (CV)0.5035642936
Kurtosis5.463025198
Mean8.299050633
Median Absolute Deviation (MAD)2
Skewness0.9766367042
Sum36715
Variance17.46492336
MonotonicityNot monotonic
2022-08-23T19:33:07.370855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
8791
17.9%
7703
15.9%
6598
13.5%
9402
9.1%
0349
7.9%
10340
7.7%
11239
 
5.4%
12223
 
5.0%
5220
 
5.0%
13140
 
3.2%
Other values (25)419
9.5%
ValueCountFrequency (%)
0349
7.9%
16
 
0.1%
28
 
0.2%
36
 
0.1%
419
 
0.4%
5220
 
5.0%
6598
13.5%
7703
15.9%
8791
17.9%
9402
9.1%
ValueCountFrequency (%)
452
< 0.1%
361
 
< 0.1%
331
 
< 0.1%
321
 
< 0.1%
311
 
< 0.1%
292
< 0.1%
281
 
< 0.1%
272
< 0.1%
264
0.1%
253
0.1%

Curricular units 1st sem (approved)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.706600362
Minimum0
Maximum26
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:07.499407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q36
95-th percentile9
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.09423798
Coefficient of variation (CV)0.6574252628
Kurtosis3.09667988
Mean4.706600362
Median Absolute Deviation (MAD)1
Skewness0.7662623978
Sum20822
Variance9.574308675
MonotonicityNot monotonic
2022-08-23T19:33:07.603400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
61171
26.5%
5723
16.3%
0718
16.2%
7471
10.6%
4433
 
9.8%
3269
 
6.1%
2160
 
3.6%
1127
 
2.9%
8108
 
2.4%
1149
 
1.1%
Other values (13)195
 
4.4%
ValueCountFrequency (%)
0718
16.2%
1127
 
2.9%
2160
 
3.6%
3269
 
6.1%
4433
 
9.8%
5723
16.3%
61171
26.5%
7471
10.6%
8108
 
2.4%
940
 
0.9%
ValueCountFrequency (%)
261
 
< 0.1%
214
 
0.1%
203
 
0.1%
192
 
< 0.1%
1815
0.3%
1710
 
0.2%
165
 
0.1%
157
 
0.2%
1414
0.3%
1326
0.6%

Curricular units 1st sem (grade)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct797
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.64082158
Minimum0
Maximum18.875
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:07.798900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median12.28571429
Q313.4
95-th percentile14.85714286
Maximum18.875
Range18.875
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation4.843663381
Coefficient of variation (CV)0.4551963724
Kurtosis0.9084610302
Mean10.64082158
Median Absolute Deviation (MAD)1.15714285
Skewness-1.568145594
Sum47074.99465
Variance23.46107495
MonotonicityNot monotonic
2022-08-23T19:33:08.141570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0718
 
16.2%
12205
 
4.6%
13147
 
3.3%
11138
 
3.1%
11.589
 
2.0%
1485
 
1.9%
12.584
 
1.9%
1082
 
1.9%
12.6666666782
 
1.9%
12.3333333382
 
1.9%
Other values (787)2712
61.3%
ValueCountFrequency (%)
0718
16.2%
9.81
 
< 0.1%
1082
 
1.9%
10.166666671
 
< 0.1%
10.28
 
0.2%
10.214285711
 
< 0.1%
10.257
 
0.2%
10.285714291
 
< 0.1%
10.3333333316
 
0.4%
10.368421051
 
< 0.1%
ValueCountFrequency (%)
18.8751
 
< 0.1%
182
 
< 0.1%
17.333333332
 
< 0.1%
17.1251
 
< 0.1%
17.111111111
 
< 0.1%
17.005555561
 
< 0.1%
175
0.1%
16.91
 
< 0.1%
16.885714291
 
< 0.1%
16.857142861
 
< 0.1%

Curricular units 1st sem (without evaluations)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1376582278
Minimum0
Maximum12
Zeros4130
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:08.254598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6908801837
Coefficient of variation (CV)5.018807771
Kurtosis89.86320828
Mean0.1376582278
Median Absolute Deviation (MAD)0
Skewness8.207403102
Sum609
Variance0.4773154283
MonotonicityNot monotonic
2022-08-23T19:33:08.350608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
04130
93.4%
1153
 
3.5%
279
 
1.8%
323
 
0.5%
415
 
0.3%
66
 
0.1%
76
 
0.1%
55
 
0.1%
84
 
0.1%
122
 
< 0.1%
ValueCountFrequency (%)
04130
93.4%
1153
 
3.5%
279
 
1.8%
323
 
0.5%
415
 
0.3%
55
 
0.1%
66
 
0.1%
76
 
0.1%
84
 
0.1%
101
 
< 0.1%
ValueCountFrequency (%)
122
 
< 0.1%
101
 
< 0.1%
84
 
0.1%
76
 
0.1%
66
 
0.1%
55
 
0.1%
415
 
0.3%
323
 
0.5%
279
1.8%
1153
3.5%

Curricular units 2nd sem (credited)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5418173599
Minimum0
Maximum19
Zeros3894
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:08.449563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.918546144
Coefficient of variation (CV)3.540946241
Kurtosis24.42726576
Mean0.5418173599
Median Absolute Deviation (MAD)0
Skewness4.634819505
Sum2397
Variance3.680819306
MonotonicityNot monotonic
2022-08-23T19:33:08.731005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
03894
88.0%
1107
 
2.4%
292
 
2.1%
478
 
1.8%
568
 
1.5%
349
 
1.1%
626
 
0.6%
1120
 
0.5%
716
 
0.4%
915
 
0.3%
Other values (9)59
 
1.3%
ValueCountFrequency (%)
03894
88.0%
1107
 
2.4%
292
 
2.1%
349
 
1.1%
478
 
1.8%
568
 
1.5%
626
 
0.6%
716
 
0.4%
812
 
0.3%
915
 
0.3%
ValueCountFrequency (%)
191
 
< 0.1%
182
 
< 0.1%
162
 
< 0.1%
152
 
< 0.1%
144
 
0.1%
139
0.2%
1214
0.3%
1120
0.5%
1013
0.3%
915
0.3%

Curricular units 2nd sem (enrolled)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.232142857
Minimum0
Maximum23
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:08.844005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median6
Q37
95-th percentile10
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.195950751
Coefficient of variation (CV)0.3523588598
Kurtosis7.134739998
Mean6.232142857
Median Absolute Deviation (MAD)1
Skewness0.7881135037
Sum27571
Variance4.822199703
MonotonicityNot monotonic
2022-08-23T19:33:08.957454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
61913
43.2%
51054
23.8%
8661
 
14.9%
7304
 
6.9%
0180
 
4.1%
1160
 
1.4%
950
 
1.1%
1048
 
1.1%
1244
 
1.0%
1337
 
0.8%
Other values (12)73
 
1.7%
ValueCountFrequency (%)
0180
 
4.1%
13
 
0.1%
25
 
0.1%
33
 
0.1%
417
 
0.4%
51054
23.8%
61913
43.2%
7304
 
6.9%
8661
 
14.9%
950
 
1.1%
ValueCountFrequency (%)
232
 
< 0.1%
211
 
< 0.1%
193
 
0.1%
182
 
< 0.1%
1712
 
0.3%
161
 
< 0.1%
152
 
< 0.1%
1422
0.5%
1337
0.8%
1244
1.0%

Curricular units 2nd sem (evaluations)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.063291139
Minimum0
Maximum33
Zeros401
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:09.064474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum33
Range33
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.947950941
Coefficient of variation (CV)0.4896202894
Kurtosis2.068285878
Mean8.063291139
Median Absolute Deviation (MAD)2
Skewness0.3364971772
Sum35672
Variance15.58631664
MonotonicityNot monotonic
2022-08-23T19:33:09.172446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
8792
17.9%
6614
13.9%
7563
12.7%
9456
10.3%
0401
9.1%
10355
8.0%
5288
 
6.5%
11255
 
5.8%
12226
 
5.1%
13126
 
2.8%
Other values (20)348
7.9%
ValueCountFrequency (%)
0401
9.1%
13
 
0.1%
24
 
0.1%
32
 
< 0.1%
410
 
0.2%
5288
 
6.5%
6614
13.9%
7563
12.7%
8792
17.9%
9456
10.3%
ValueCountFrequency (%)
331
 
< 0.1%
281
 
< 0.1%
272
 
< 0.1%
263
 
0.1%
251
 
< 0.1%
243
 
0.1%
234
 
0.1%
2210
0.2%
2110
0.2%
208
0.2%

Curricular units 2nd sem (approved)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.435804702
Minimum0
Maximum20
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:09.286654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.014763902
Coefficient of variation (CV)0.6796430648
Kurtosis0.8450446589
Mean4.435804702
Median Absolute Deviation (MAD)2
Skewness0.3062793835
Sum19624
Variance9.088801387
MonotonicityNot monotonic
2022-08-23T19:33:09.389153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6965
21.8%
0870
19.7%
5726
16.4%
4414
9.4%
7331
 
7.5%
8321
 
7.3%
3285
 
6.4%
2198
 
4.5%
1114
 
2.6%
1148
 
1.1%
Other values (10)152
 
3.4%
ValueCountFrequency (%)
0870
19.7%
1114
 
2.6%
2198
 
4.5%
3285
 
6.4%
4414
9.4%
5726
16.4%
6965
21.8%
7331
 
7.5%
8321
 
7.3%
936
 
0.8%
ValueCountFrequency (%)
202
 
< 0.1%
193
 
0.1%
182
 
< 0.1%
178
 
0.2%
162
 
< 0.1%
146
 
0.1%
1321
0.5%
1234
0.8%
1148
1.1%
1038
0.9%

Curricular units 2nd sem (grade)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct782
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.23020572
Minimum0
Maximum18.57142857
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:09.595152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.75
median12.2
Q313.33333333
95-th percentile14.9802619
Maximum18.57142857
Range18.57142857
Interquartile range (IQR)2.58333333

Descriptive statistics

Standard deviation5.210807955
Coefficient of variation (CV)0.5093551485
Kurtosis0.0665673513
Mean10.23020572
Median Absolute Deviation (MAD)1.2
Skewness-1.313650168
Sum45258.43012
Variance27.15251954
MonotonicityNot monotonic
2022-08-23T19:33:09.806190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0870
 
19.7%
12170
 
3.8%
11165
 
3.7%
13150
 
3.4%
11.586
 
1.9%
12.584
 
1.9%
1477
 
1.7%
1077
 
1.7%
13.565
 
1.5%
12.6666666761
 
1.4%
Other values (772)2619
59.2%
ValueCountFrequency (%)
0870
19.7%
1077
 
1.7%
10.166666674
 
0.1%
10.24
 
0.1%
10.2510
 
0.2%
10.3333333319
 
0.4%
10.3751
 
< 0.1%
10.48
 
0.2%
10.428571432
 
< 0.1%
10.444444442
 
< 0.1%
ValueCountFrequency (%)
18.571428571
< 0.1%
17.714285711
< 0.1%
17.692307691
< 0.1%
17.62
< 0.1%
17.58751
< 0.1%
17.428571431
< 0.1%
17.166666671
< 0.1%
172
< 0.1%
16.909090911
< 0.1%
16.82
< 0.1%

Curricular units 2nd sem (without evaluations)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1503164557
Minimum0
Maximum12
Zeros4142
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:09.917979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7537740685
Coefficient of variation (CV)5.014581172
Kurtosis66.81169195
Mean0.1503164557
Median Absolute Deviation (MAD)0
Skewness7.267700851
Sum665
Variance0.5681753464
MonotonicityNot monotonic
2022-08-23T19:33:10.019975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
04142
93.6%
1140
 
3.2%
248
 
1.1%
335
 
0.8%
421
 
0.5%
517
 
0.4%
68
 
0.2%
86
 
0.1%
75
 
0.1%
122
 
< 0.1%
ValueCountFrequency (%)
04142
93.6%
1140
 
3.2%
248
 
1.1%
335
 
0.8%
421
 
0.5%
517
 
0.4%
68
 
0.2%
75
 
0.1%
86
 
0.1%
122
 
< 0.1%
ValueCountFrequency (%)
122
 
< 0.1%
86
 
0.1%
75
 
0.1%
68
 
0.2%
517
 
0.4%
421
 
0.5%
335
 
0.8%
248
 
1.1%
1140
 
3.2%
04142
93.6%

Unemployment rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.56613924
Minimum7.6
Maximum16.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2022-08-23T19:33:10.114979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile7.6
Q19.4
median11.1
Q313.9
95-th percentile16.2
Maximum16.2
Range8.6
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.663850484
Coefficient of variation (CV)0.2303145785
Kurtosis-0.9955259057
Mean11.56613924
Median Absolute Deviation (MAD)1.7
Skewness0.2120510532
Sum51168.6
Variance7.096099403
MonotonicityNot monotonic
2022-08-23T19:33:10.198977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7.6571
12.9%
9.4533
12.0%
10.8525
11.9%
12.4445
10.1%
12.7419
9.5%
11.1414
9.4%
15.5397
9.0%
13.9390
8.8%
8.9368
8.3%
16.2362
8.2%
ValueCountFrequency (%)
7.6571
12.9%
8.9368
8.3%
9.4533
12.0%
10.8525
11.9%
11.1414
9.4%
12.4445
10.1%
12.7419
9.5%
13.9390
8.8%
15.5397
9.0%
16.2362
8.2%
ValueCountFrequency (%)
16.2362
8.2%
15.5397
9.0%
13.9390
8.8%
12.7419
9.5%
12.4445
10.1%
11.1414
9.4%
10.8525
11.9%
9.4533
12.0%
8.9368
8.3%
7.6571
12.9%

Inflation rate
Real number (ℝ)

HIGH CORRELATION

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.228028933
Minimum-0.8
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative923
Negative (%)20.9%
Memory size34.7 KiB
2022-08-23T19:33:10.287977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile-0.8
Q10.3
median1.4
Q32.6
95-th percentile3.7
Maximum3.7
Range4.5
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.382710692
Coefficient of variation (CV)1.125959376
Kurtosis-1.039033386
Mean1.228028933
Median Absolute Deviation (MAD)1.2
Skewness0.2523753519
Sum5432.8
Variance1.911888856
MonotonicityNot monotonic
2022-08-23T19:33:10.370974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1.4893
20.2%
2.6571
12.9%
-0.8533
12.0%
0.5445
10.1%
3.7419
9.5%
0.6414
9.4%
2.8397
9.0%
-0.3390
8.8%
0.3362
8.2%
ValueCountFrequency (%)
-0.8533
12.0%
-0.3390
8.8%
0.3362
8.2%
0.5445
10.1%
0.6414
9.4%
1.4893
20.2%
2.6571
12.9%
2.8397
9.0%
3.7419
9.5%
ValueCountFrequency (%)
3.7419
9.5%
2.8397
9.0%
2.6571
12.9%
1.4893
20.2%
0.6414
9.4%
0.5445
10.1%
0.3362
8.2%
-0.3390
8.8%
-0.8533
12.0%

GDP
Real number (ℝ)

HIGH CORRELATION

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00196880651
Minimum-4.06
Maximum3.51
Zeros0
Zeros (%)0.0%
Negative1711
Negative (%)38.7%
Memory size34.7 KiB
2022-08-23T19:33:10.460975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.06
5-th percentile-4.06
Q1-1.7
median0.32
Q31.79
95-th percentile3.51
Maximum3.51
Range7.57
Interquartile range (IQR)3.49

Descriptive statistics

Standard deviation2.269935441
Coefficient of variation (CV)1152.949988
Kurtosis-1.001653245
Mean0.00196880651
Median Absolute Deviation (MAD)1.47
Skewness-0.3940682069
Sum8.71
Variance5.152606907
MonotonicityNot monotonic
2022-08-23T19:33:10.545977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.32571
12.9%
-3.12533
12.0%
1.74525
11.9%
1.79445
10.1%
-1.7419
9.5%
2.02414
9.4%
-4.06397
9.0%
0.79390
8.8%
3.51368
8.3%
-0.92362
8.2%
ValueCountFrequency (%)
-4.06397
9.0%
-3.12533
12.0%
-1.7419
9.5%
-0.92362
8.2%
0.32571
12.9%
0.79390
8.8%
1.74525
11.9%
1.79445
10.1%
2.02414
9.4%
3.51368
8.3%
ValueCountFrequency (%)
3.51368
8.3%
2.02414
9.4%
1.79445
10.1%
1.74525
11.9%
0.79390
8.8%
0.32571
12.9%
-0.92362
8.2%
-1.7419
9.5%
-3.12533
12.0%
-4.06397
9.0%

Target
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Graduate
2209 
Dropout
1421 
Enrolled
794 

Length

Max length8
Median length8
Mean length7.678797468
Min length7

Characters and Unicode

Total characters33971
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDropout
2nd rowGraduate
3rd rowDropout
4th rowGraduate
5th rowGraduate

Common Values

ValueCountFrequency (%)
Graduate2209
49.9%
Dropout1421
32.1%
Enrolled794
 
17.9%

Length

2022-08-23T19:33:10.651938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-23T19:33:10.760945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
graduate2209
49.9%
dropout1421
32.1%
enrolled794
 
17.9%

Most occurring characters

ValueCountFrequency (%)
r4424
13.0%
a4418
13.0%
o3636
10.7%
u3630
10.7%
t3630
10.7%
d3003
8.8%
e3003
8.8%
G2209
6.5%
l1588
 
4.7%
D1421
 
4.2%
Other values (3)3009
8.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29547
87.0%
Uppercase Letter4424
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r4424
15.0%
a4418
15.0%
o3636
12.3%
u3630
12.3%
t3630
12.3%
d3003
10.2%
e3003
10.2%
l1588
 
5.4%
p1421
 
4.8%
n794
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
G2209
49.9%
D1421
32.1%
E794
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Latin33971
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r4424
13.0%
a4418
13.0%
o3636
10.7%
u3630
10.7%
t3630
10.7%
d3003
8.8%
e3003
8.8%
G2209
6.5%
l1588
 
4.7%
D1421
 
4.2%
Other values (3)3009
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII33971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r4424
13.0%
a4418
13.0%
o3636
10.7%
u3630
10.7%
t3630
10.7%
d3003
8.8%
e3003
8.8%
G2209
6.5%
l1588
 
4.7%
D1421
 
4.2%
Other values (3)3009
8.9%

Interactions

2022-08-23T19:32:44.037650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:51.739198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:56.694681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:01.083852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:06.642662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:11.297967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:15.739970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:19.801423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:23.960997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:28.472092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:33.024522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:37.838168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:42.051940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:46.627775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:50.854016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:55.495186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:59.124473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:02.750313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:06.324675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:10.271948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:13.777158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:17.587494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:21.753041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:25.289364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:29.061880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:32.668918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:37.221141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:40.477143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:44.175657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:51.940854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:56.819697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:01.289849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:06.829076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:11.496972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:15.867171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:19.924745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:24.080119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:28.626916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:33.201058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:37.968421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:42.177230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:46.822100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:50.991968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:55.628309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:59.238789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:02.872565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:06.449158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:10.393176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:13.898369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:17.706643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:21.896129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:25.448905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:29.185020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:32.783899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:37.336142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:40.595144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:44.308653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:52.101556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:56.977828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:01.480989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:06.974740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:11.661217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:15.985662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:20.045464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:24.220350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:28.770844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:33.434162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:38.099022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:42.293637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:47.040753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:51.123928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:55.751898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:59.357635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:02.986996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:06.837584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:10.519596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:14.020432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:17.817697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:22.016946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:25.577703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:29.300040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:32.900468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:37.443112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:40.702111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:44.428646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:52.263843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:57.133730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:01.634635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:07.104546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:11.786687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:16.132062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:20.172966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:24.350207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:28.901481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:33.651024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:38.243794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:42.414804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:47.182017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:51.239363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:55.873667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-08-23T19:32:43.579676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:47.277725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:56.033586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:00.619954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:05.959714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:10.761590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:15.311725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:19.264184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:23.545727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:28.050432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:32.498790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:37.401914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:41.595215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:46.116606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:50.457908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:54.873762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:58.750634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:02.397894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:05.975971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:09.733179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:13.413554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:17.225448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:21.400640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:24.910231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:28.692196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:32.285298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:36.726728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:40.146111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:43.697717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:47.388719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:56.158346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:00.758549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:06.179949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:10.960346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:15.484809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:19.390791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:23.675296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:28.171804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:32.668082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:37.550841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:41.733820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:46.299077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:50.585291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:55.114509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:58.878985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:02.518634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:06.090627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:10.033398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:13.538591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:17.338493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:21.522169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:25.038747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:28.810784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:32.401817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:36.966729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:40.255146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:43.800670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:47.496631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:30:56.529365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:00.900852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:06.401840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:11.135333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:15.622202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:19.528102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:23.823548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:28.335589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:32.820458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:37.696043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:41.879675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:46.439094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:50.709996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:55.328257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:31:59.004600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:02.628580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:06.205196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:10.148801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:13.653564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:17.458155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:21.633715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:25.163125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:28.933300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:32.518340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:37.088727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:40.364129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-23T19:32:43.915655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-08-23T19:33:11.579214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-23T19:33:12.501678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-23T19:33:13.054678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-23T19:33:13.623528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-23T19:33:14.357667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-23T19:32:49.586648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-23T19:32:50.990768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Marital statusApplication modeApplication orderCourseDaytime/evening attendancePrevious qualificationPrevious qualification (grade)NacionalityMother's qualificationFather's qualificationMother's occupationFather's occupationAdmission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTarget
0117517111122.01191259127.310011020000000.000000000000.000000010.81.41.74Dropout
11151925411160.011333142.5100010190066614.0000000066613.666667013.9-0.30.79Graduate
2115907011122.01373799124.810001019006000.000000006000.000000010.81.41.74Dropout
31172977311122.01383753119.6100100200068613.42857100610512.40000009.4-0.8-3.12Graduate
42391801401100.01373899141.5000100450069512.3333330066613.000000013.9-0.30.79Graduate
523919991019133.11373797114.80011105000510511.85714300517511.500000516.20.3-0.92Graduate
6111950011142.011938710128.4100101180079713.3000000088814.345000015.52.8-4.06Graduate
71184925411119.01373799113.110001022005500.000000005500.000000015.52.8-4.06Dropout
8113923811137.0621199129.3000101211068613.8750000067614.142857016.20.3-0.92Graduate
9111923811138.0111947123.0101000180069511.40000000614213.50000008.91.43.51Dropout

Last rows

Marital statusApplication modeApplication orderCourseDaytime/evening attendancePrevious qualificationPrevious qualification (grade)NacionalityMother's qualificationFather's qualificationMother's occupationFather's occupationAdmission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTarget
4414111913011137.0133835129.3100100180056511.8000001058511.60000009.4-0.8-3.12Graduate
441543919500119133.11373766117.80010004600714312.33333300712311.083333011.10.62.02Dropout
44161432950011136.01383895131.30001002301114151212.62500011114151212.62500017.62.60.32Graduate
4417111907011132.011199133.8100101200066613.8333330066613.500000016.20.3-0.92Graduate
441814419070139120.0133839120.0000110200277612.50000005910713.142857116.20.3-0.92Graduate
4419116977311125.011154122.2000110190067513.6000000068512.666667015.52.8-4.06Graduate
4420112977311120.01051199119.0101000181066612.0000000066211.000000011.10.62.02Dropout
4421111950011154.01373799149.5100101300078714.9125000089113.500000013.9-0.30.79Dropout
4422111914711180.01373774153.8100101200055513.8000000056512.00000009.4-0.8-3.12Graduate
44231101977311152.022383759152.0100100221068611.6666670066613.000000012.73.7-1.70Graduate